Method, computer program product, and apparatus for deriving intelligence from format change requests

ABSTRACT

Data indicating a problem in a network is derived by collecting reformatting requests from devices in the network. The reformatting requests are generated by the devices in response to problems the devices experience in communicating using the network. The reformatting requests are correlated to determine a pattern in the reformatting requests over a period of time, and data indicating a problem in the network is derived based on the pattern.

BACKGROUND

Exemplary embodiments relate generally to deriving information and, inparticular, deriving information from a network.

With the advent of fixed/mobile convergence, it will become increasinglylikely that the quality of service experienced by a customer will varyas a multimedia session is redirected to different devices (e.g., PCs,TVs, wireless PDAs) connected using different technologies (e.g.,broadband, wireless) through varying locations.

SUMMARY

It should be appreciated that this Summary is provided to introduce aselection of concepts in a simplified form, the concepts being furtherdescribed below in the Detailed Description. This Summary is notintended to identify key features or essential features of thisdisclosure, nor is it intended to limit the scope of the invention.

According to an exemplary embodiment, a method for deriving dataindicating a problem in a network includes collecting reformattingrequests from devices in the network. The reformatting requests aregenerated by the devices in response to problems the devices experiencein communicating using the network. The method further includescorrelating, via a server, the reformatting requests to determine apattern in the reformatting requests over a period of time. Dataindicating a problem in the network is derived based on the pattern.

According to another embodiment, a computer program product includes acomputer readable storage medium having encoded instructions storedthereon for deriving data indicating a problem in a network. Theinstructions, when executed by a computer, cause the computer to collectreformatting requests from devices in the network. The reformattingrequests are generated by the devices in response to problems thedevices experience in communicating using the network. The reformattingrequests are correlated to determine a pattern in the reformattingrequests over a period of time. Data indicating a problem in the networkis derived based on the pattern.

According to another embodiment, a system for deriving data indicating aproblem in a network includes an input for receiving reformattingrequests from devices in the network. The reformatting requests aregenerated by the devices in response to problems the devices experiencein communicating using the network. The system further includes acorrelator for correlating the reformatting requests to determine apattern in the reformatting requests over a period of time. Dataindicating a problem in the network is derived based on the pattern.

According to another embodiment, other methods, computer programproducts, and/or systems according to various embodiments will be orbecome apparent to one with skill in the art upon review of thefollowing drawings and detailed description. It is intended that allsuch additional systems, methods, and/or computer program products beincluded within this description, be within the scope of the presentinvention, and be protected by the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for collecting data, correlating thecollected data, and deriving data indicating a problem in a networkaccording to an exemplary embodiment.

FIG. 2 is a flowchart illustrating a method for deriving data indicatinga problem in a network according to an exemplary embodiment.

FIG. 3 illustrates an exemplary device for deriving data indicating aproblem in a network according to an exemplary embodiment.

DETAILED DESCRIPTION

Exemplary embodiments will be described more fully hereinafter withreference to the accompanying figures, in which embodiments are shown.This invention may, however, be embodied in many alternate forms andshould not be construed as limited to the embodiments set forth herein.

Exemplary embodiments are described below with reference to blockdiagrams and/or flowchart illustrations of methods, apparatus (systemsand/or devices) and/or computer program products. It should beunderstood that a block of the block diagrams and/or flowchartillustrations, and combinations of blocks in the block diagrams and/orflowchart illustrations, can be implemented by computer programinstructions. These computer program instructions may be provided to aprocessor of a general purpose computer, special purpose computer,digital signal processor and/or other programmable data processingapparatus to produce a machine, such that the instructions, whichexecute via the processor of the computer and/or other programmable dataprocessing apparatus, create means (functionality) and/or structure forimplementing the functions/acts specified in the block diagrams and/orflowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a processor of the computerand/or other programmable data processing apparatus to function in aparticular manner, such that the instructions stored in thecomputer-readable memory produce an article of manufacture includinginstructions which implement the function/act as specified in the blockdiagrams and/or flowchart block or blocks.

The computer program instructions may also be loaded onto a computerand/or other programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer and/or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions which execute on the computer or otherprogrammable apparatus provide steps for implementing the functions/actsspecified in the block diagrams and/or flowchart block or blocks.

Accordingly, exemplary embodiments may be implemented in hardware and/orin software (including firmware, resident software, micro-code, etc.)that runs on a processor such as a digital signal processor,collectively referred to as “circuitry” or “a circuit”. Furthermore,exemplary embodiments may take the form of a computer program productcomprising a computer-usable or computer-readable storage medium havingcomputer-usable or computer-readable program code embodied in the mediumfor use by or in connection with an instruction execution system. In thecontext of this document, a computer-usable or computer-readable mediummay be any medium that can contain, store, communicate or transport theprogram for use by or in connection with the instruction executionsystem, apparatus, or device.

The computer-usable or computer-readable medium may be, for example butnot limited to, an electronic, magnetic, optical, electromagnetic orsemiconductor system, apparatus or device. More specific examples (anon-exhaustive list) of the computer-readable medium would include thefollowing: a portable computer diskette, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), and a portable optical and/or magnetic media, such asa flash disk or CD-ROM.

It should also be noted that in some alternate implementations, thefunctions/acts noted in the blocks may occur out of the order noted inthe flowcharts. For example, two blocks shown in succession may in factbe executed substantially concurrently or the blocks may sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved. Moreover, the functionality of a given block of the flowchartsand/or block diagrams may be separated into multiple blocks and/or thefunctionality of two or more blocks of the flowcharts and/or blockdiagrams may be at least partially integrated. Finally, other blocks maybe added/inserted between the blocks that are illustrated.

There exist a number of intelligent end user devices, also referred toherein as receivers, which use various wired and wireless accesstechnologies. Many of these devices have the capability to receivemultimedia streams. A stream is formatted for a particular receiverbased upon the capabilities of the device and the characteristics of thecommunication channel over which the stream is transmitted. Asconditions in the communication channel vary, a multimedia stream mayrequire reformatting. For instance, if there is congestion in one ormore network links or there is impairment in a wireless channel,receivers may request that streams be transmitted at a lower bit rate inorder to ensure uninterrupted delivery of data. Another option may be toswitch to a different (perhaps more complex/costly) encoding format thathas a greater tolerance to particular types of impairment, such asdelay, bit errors or jitter. In all these cases, time-varying networkconditions may be adapted by trading off one parameter (e.g., processingcomplexity/cost, picture resolution, or audio fidelity) to improve theend-user's overall quality of experience. Such action requires end userdevices or other network elements to recognize that the experience isbeing “impaired” and to request the network to modify the streamaccordingly.

Depending on its level of sophistication, an end user device mayrecognize individual impairment types and request a particular formatthat better tolerates the impairment being experienced. Regardless ofthe exact mechanism used to initiate a “reformat request,” any suchrequest indicates a failure or degradation somewhere in the end-to-endarrangement, from the source of the multimedia stream through all thevarious connectivity technologies, to the receiver. By collecting andcorrelating data related to requests for stream reformatting,information about the likely cause(s) of these requests may be derived,and the cause may be isolated and resolved.

If service quality degrades beyond an acceptable threshold, customerequipment (end user device) or network equipment can request themultimedia stream be modified into a format better suited to thechanging conditions thereby providing a better quality of experience.While this may correct the problem for an individual customer or anindividual network element, it does not prevent other customers ornetwork elements, operating under similar conditions, states, locations,equipment and the like, from experiencing the same problem. Thisdisclosure uses aggregate information derived from a set of correctiveactions (like that described above) to identify the cause and/orlocation of these problems, thus enabling proactive preventive measures

According to exemplary embodiments, network and device performance andoperational information is extracted from requests to reformatmultimedia streams. Such requests result from the detection of lowquality of experience by either end-user devices or network-basedequipment. According to an exemplary embodiment, poor quality in amultimedia stream is detected, and a different, e.g., more robust,format is requested in order to improve the customer/network elementexperience. Poor performance and/or quality may be automaticallyrecognized, and a reformatting request may be automatically initiated,or poor performance/quality may be recognized, and a reformattingrequest may be generated manually, e.g., by an end user. Elements areprovided that are capable of recognizing and responding to theserequests and creating a “data point” associated with each request. Thedata points contain information about the format change request, such asthe device making the request, the time at which the format changerequest was made, software, firmware and hardware versions, location ofthe device making request, transmission technologies used to transportthe request, original format, new format, original bit rate, new bitrate, specific network elements traversed by the stream, etc.). Datacollector(s) gather/accept these data points and correlation engine(s)can sort these data elements and identify patterns which indicatespecific trouble(s). Information about the troubles identified may becommunicated to a device, such as an operations center, such theremedial action may be taken.

FIG. 1 illustrates a system for collecting data, correlating thecollected data, and deriving data indicating a problem in a networkaccording to an exemplary embodiment. The system includes various enduser devices 110 a, 110 b, 110 c and 110 d and network elements 115 aand 115 b in communication with one or more collectors 120 a and 120 b.The end user devices may include various types of communication devices,including but not limited to television sets, radios, personalcomputers, cellular telephones, video conferencing equipment, audiobridges, and hand-held mobile televisions capable of receiving andtransmission multimedia streams.

The network elements 115 a, 115 b may include devices such astransceivers, switches, and encoders. The encoders convert incomingmultimedia data streams into output of a specific format and bit rate.Examples of formats include Moving Pictures Expert Group Layer-3 Audio(MP3) MP3 and Advanced Audio Coding (AAC) for audio, Moving PicturesExpert Group Layer-2 Video (MPEG-2) and Moving Pictures Expert GroupLayer-4 Video (MPEG-2) for video, and Enhanced Full Rate (EFR) andAdaptive Multi-Rate (AMR) for cellular. The end user devices 110 a, 110b, 110 c, and 110 d and the network elements 115 a and 115 b maycommunicate with each other and with collectors 120 a and 120 b viacommunications channels, such as fiber optic cables, copper wires andvarious wireless technologies. Multimedia streams may flow over thesechannels.

For purposes of this disclosure, multimedia streams may be considered ascontinuous flows of content. Examples of multimedia streams includeaudio streams (e.g., radio programming, phone calls) or audio/videostreams (e.g., television programming, multimedia conference calls).Multimedia streams may also contain only video information (e.g.,traffic webcam) or alphanumeric information (e.g., stock quotes, sportsscores).

Although not shown in FIG. 1 for simplicity of illustration, it shouldbe appreciated that the network may also include content sources, e.g.,content providers. Also, the end user devices may be considered contentsources.

From time to time, deficiencies or failures may occur in componentelements of a communications channel. These deficiencies or failures,also referred to as impairments, may include such things as loss of atransmission facility due to equipment failure, break in optical cable,excessive delay or variation in delay, high bit error rates, etc.

The collectors 120 a and 120 b record information (data points) from theend user devices 110 a, 110 b, 110 c, and 110 d and network elements 115a and 115 b within the communication network. According to exemplaryembodiment, the data collected by the collectors 120 a and 120 b may bereformatting requests generated by the end user devices or networkelements. According to exemplary embodiments, the extraction andintelligent processing of network and device performance and operationalinformation may be performed based on requests to reformat multimediastreams. Such requests result from the detection of low quality ofexperience by either end-user devices or network-based equipment.

The collected data is delivered to a correlation engine 130. Thecorrelation engine 130, which may be implemented as a server, analyzes aset of data points to identify common characteristics and derive dataindicating a problem in the communications network. Some examples ofcharacteristics which may be included in data points are communicationchannels, network elements, end-user devices, sources, terrain,environment conditions, and time of day, format and bit rate used bystreams. The data points may be collected over a period of time, e.g., aweek or a number of hours, and the correlator 130 determines a patternin the data points. Based on the pattern, a problem in the network,e.g., a dead spot in a cellular network, a time of day during whichcapacity needs to be increased, or a device problem, may be discerned.By correlating the data points, the correlation engine 130 produces dataindicative of this problem such that an operations center can act on theprogram and take remedial actions as appropriate, e.g., repair the deadspot, increase network bandwidth as needed, indicate to the end userthat there is a problem with the end user device, etc.

Although shown as separate devices for ease of understanding, it shouldbe appreciated that the collectors and the correlation engine may beintegrated into one or more devices. Also, it should be appreciated thatthe communication network may include any number of end user devices andnetwork elements.

FIG. 2 is a flowchart illustrating a method for deriving data indicatinga problem in a network according to an exemplary embodiment. At step210, reformatting requests are collected by, e.g., collectors 120 a and120 b. The reformatting requests may be generated by the end userdevices, e.g., devices 110 a, 110 b, 110 c and/or 110 d, or networkelements, e.g., network element 115 a and/or network element 115 b. Thereformatting requests are correlated to derive data indicating a problemin the network at step 220. This correlation may be performed incorrelation engine 130. Data indicating a problem in the network ispresented at step 230, such that remedial action may be taken to resolvethe problem.

FIG. 3 illustrates an exemplary device for deriving data indicating aproblem in a network according to an exemplary embodiments. Referringnow to FIG. 3, the system 300 includes a processor 340, a transceiver350, and a memory 305. The processor 340 communicates with the memory305 via an address/data bus. The processor 340 may be, for example, acommercially available or custom microprocessor. The memory 305 isrepresentative of the one or more memory devices containing the softwareand data used to facilitate application of the rules for access by anobserver device to a communication session in accordance with someembodiments. The memory 305 may include, but is not limited to, thefollowing types of devices: cache, ROM, PROM, EPROM, EEPROM, flash,SRAM, and DRAM. The transceiver 350 includes a transmitter circuit and areceiver circuit, which are used to establish and maintain communicationwith other devices, such as the collectors 110 a and 110 b shown in FIG.1, and devices used for taking remedial actions, such as a networkcontrol center?.

As shown in FIG. 3, the memory 305 may contain multiple categories ofsoftware and/or data: an operating system 310, a correlation rulesmodule 320, and a communication module 330. The operating system 310generally controls the operation of the correlation rules module 320. Inparticular, the operating system 310 may manage the correlation rulesmodule's software and/or hardware resources and may coordinate executionof programs by the processor 340. The communication module 330 may beconfigured to manage the communication protocols, including bothwireless and wireline protocols that may be used by the transceiver 350to communicate with other devices in the communication network, such asthe collectors 120 a and 120 b shown in FIG. 1. The communicationprotocols may include, but are not limited to TCP/IP, H.323, etc. Thecommunication module 330 may control conversion of signals received inone type of communication protocols into an appropriate format/protocolfor transmission to other devices.

Although FIG. 3 illustrates an exemplary device for correlatingreformatting requests to derive data indicating a problem in a network,in accordance with some embodiments, it will be understood that thepresent invention is not limited to such a configuration but is intendedto encompass any configuration capable of carrying out operationsdescribed herein.

As an aid to understanding exemplary embodiments, examples are presentedbelow. In the following examples, the source of the multimedia stream isassumed to a content provider within the communication network, and thereceivers are considered to be the end user customer/subscriber devices.The reverse may also be true. That is, the source of the signal may bean end user (customer or subscriber) device, while the receiver may anetwork element within the service provider's network. The correlationengine processes data collected from the receivers, irrespective oftheir location.

In a first example, congestion occurs on a network link. This examplemay be understood by considering a number of residential customers orusers of end user devices, e.g. devices 110 a, 110 b, 110 c, 110 d,watching streams originating in a communication network via high speedInternet connections. The video quality begins to degrade as determinedby the receiver (end user device). The end user device requests that thesource send the transmission at a lower bit rate. A data collector,e.g., data collectors 120 a, 120 b within the network gathersinformation about multimedia stream bit rate changes. The collectorsends its information to a correlation engine, which also has receivedinformation about network topology. The correlation engine, e.g.,correlator 130, maps a number of receivers requesting a lower bit rateto a specific set of links within the network. An alert may be sent fromthe correlator 130 to an operations center indicating possiblecongestion on the identified links.

In a second example, there is a malfunction of a wireless networkelement. To illustrate this example, consider various mobile devices,e.g., end user devices 110 a, 110 b, 110 c, 110 d, receiving multimediastreams from a communication network. The devices are in motion. As theymove, they associate with a cell site, and immediately stream qualitybegins to degrade. The mobile devices request that the streams betransmitted at a lower bit rate. A data collector within the network,e.g., data collectors 120 a, 120 b, gathers information about the bitrange changes and sends that information to a correlation engine, e.g.,correlator 130. The correlation engine determines that a high percentageof wireless devices associated with the particular cell site requestlower bit rates. An alert may be sent from the correlator 130 to anoperations center indicating that there is a possible problem with thecell site.

In a third example, there is an encoder malfunction. To illustrate thisexample, consider many devices receiving a multimedia stream. Thecustomers or users of the devices request that the source send thestream at a lower bit rate. Data collectors, e.g., data collectors 120a, 120 b, record the requests for a lower bit rate and forward thatinformation to a correlation engine, e.g., correlator 130. Thecorrelation engine determines that a high percentage of devices on allnetworks receiving a stream originating from a particular encoderrequest a lower bit rate. An alert may be sent from the correlator 130to the operations center indicating that there is a possible problemwith the encoder.

In a fourth example, there is an end user device problem. To illustratethis example, consider reformat request data collected over time(perhaps as long as weeks or months) that shows a strong correlation toa particular brand and model (and perhaps hardware vintage and/orsoftware version) wireless device. From the data collected, thecorrelator 130 determines that it is apparent that the end user devices,e.g., devices 110 a, 110 b, 110 c, and/or 110 d, have (perhapsunacceptably) low tolerance to certain types of channel impairments.Based on this, a service provider may take any of several steps such as:informing the device provider of the suspected problem and requesting itbe corrected, discontinuing sales of the device, informing users of thedevice limitation and recommending the user reconfigure the device,avoid use of certain features/capabilities, exchange the device for adifferent vintage, etc, changing the default encoding format (or deviceoption settings or the like) used for this device to one that does notexhibit the problem or provides a better user experience, upgrading thedevice to a new software version that corrects the problem or lessensits impact on the user thus reducing the volume of format changerequests, and/or modifying network infrastructure to reduce/eliminatethe trouble-inducing impairment.

In a fifth example, a problem occurs due to stream source capacity. Inthis example, consider reformat request data collected by, e.g.,collectors 110 a, 110 b, 110 c, and 110 d over the course of a day (orseveral days). The correlator 130 determines that the collected dataindicates a degradation of multimedia streams originating from aparticular location (e.g., an individual server, data center, accesslink), perhaps occurring at the same time each day. Based on theparticular circumstances, the service provider may take any number ofsteps such as: recommending capacity enhances (e.g., more servers,higher capacity access line, different encoding format) to the contentprovider. If the service provider is also providing “hosting services”to the content originator, it may proactively take any of the stepslisted above, or recommend an alternative solution to the contentoriginator, such as an intelligent content distribution service, and/oralert customer care personnel (or provide real-time status on a website)who can then inform end-users of the trouble source in response tocomplaints.

In a sixth example, an encoding format problem occurs. In this example,assume that a new encoding format is introduced in the network. Overtime, a pattern of format change requests collected by, e.g., collectors110 a, 110 b, 110 c, 110 d is determined (by a correlation engine 130)which indicates that this format does not perform well under certainconditions, such as particular transmission channel type(s), terrains(e.g., hilly, water surfaces) or environments (e.g., urban canyons),climatic conditions, or a combination of these factors. Knowledge ofsuch limitations may potentially be used to avoid use of that formatunder problematic conditions or environments, instigate improvements tothe format, eliminate use of this format, etc.

According to an exemplary embodiment, a system and technique areprovided for root cause analysis of recurring troubles associated withnon-stationary multimedia sessions. This technique allows informationabout network or equipment troubles to be derived from the cumulativetroubles experienced by a number of individual, unrelatedcustomers/users occurring over a period of time. Without thiscapability, these troubles would take longer to recognize or may not beidentified at all. Once identified, proactive preventive measures can betaken to eliminate or avoid these troubles, thus improving customersatisfaction and loyalty, as well as reducing expense related to troubleisolation and correction.

Computer program code for carrying out operations of devices, terminals,and/or systems discussed above with respect to FIGS. 1-3 may be writtenin a high level programming language, such as Java, C, and/or C++, fordevelopment convenience. In addition, computer program code for carryingout operations of embodiments may also be written in other programminglanguages, such as, but not limited to, interpreted languages. Somemodules or routines may be written in assembly language or even microcode to enhance performance and/or memory usage. It will be furtherappreciated that the functionality of any or all of the program modulesmay also be implemented using discrete hardware components, one or moreapplication specific integrated circuits (ASICs), or a programmeddigital signal processor or microcontroller.

Exemplary embodiments are described herein with reference to messageflow, flowchart and/or block diagram illustrations of methods, devices,and/or computer program products. These message flow, flowchart and/orblock diagrams further illustrate exemplary operations for performingcorrelation of reformatting request to derive data indicating a problemin a network in accordance with various embodiments. It will beunderstood that each message/block of the message flow, flowchart and/orblock diagram illustrations, and combinations of messages/blocks in themessage flow, flowchart and/or block diagram illustrations, may beimplemented by computer program instructions and/or hardware operations.These computer program instructions may be provided to a processor of ageneral purpose computer, a special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions, which execute via the processor of the computer orother programmable data processing apparatus, create means forimplementing the functions specified in the message flow, flowchartand/or block diagram block or blocks.

These computer program instructions may also be stored in a computerusable or computer-readable memory that may direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer usable orcomputer-readable memory produce an article of manufacture includinginstructions that implement the function specified in the message flow,flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions that execute on the computer or other programmableapparatus provide steps for implementing the functions specified in themessage flow, flowchart and/or block diagram block or blocks.

Many different embodiments have been disclosed herein, in connectionwith the above description and the drawings. It will be understood thatit would be unduly repetitious and obfuscating to literally describe andillustrate every combination and subcombination of these embodiments.Accordingly, the present specification, including the drawings, shall beconstrued to constitute a complete written description of allcombinations and subcombinations of the embodiments described herein,and of the manner and process of making and using them, and shallsupport claims to any such combination or subcombination.

In the drawings and specification, there have been disclosed embodimentsof the invention and, although specific terms are employed, they areused in a generic and descriptive sense only and not for purposes oflimitation, the scope of the invention being set forth in the followingclaims.

What is claimed is:
 1. A method, for deriving data indicating a problemin a network, comprising: collecting, by a server, reformatting requestsfrom devices in the network, wherein the reformatting requests aregenerated by the devices in response to impairments the devicesexperience in communicating using the network; aggregating, by theserver, the reformatting requests collected over a period of time;analyzing, by the server, the aggregated reformatting requests collectedover the period of time to identify common characteristics indicating apattern in the reformatting requests collected over the period of time;and deriving, by the server, data indicating the problem in the networkbased on the pattern.
 2. The method of claim 1, wherein the devicesinclude at least one of end user devices and network devices.
 3. Themethod of claim 1, wherein the problem is caused by at least one of anend user device and a network device.
 4. The method of claim 1, whereinthe problem is a lack of capacity in the network for handling traffic.5. The method of claim 1, wherein the network is a multimedia network.6. The method of claim 1, wherein the network is a cellular network. 7.The method of claim 1, wherein remedial actions are taken based on thederived data.
 8. A non-transitory computer readable storage devicehaving instructions encoded thereon for deriving data indicating aproblem in a network, the instructions, when executed by a computer,causing the computer to perform operations comprising: collectingreformatting requests from devices in the network, wherein thereformatting requests are generated by the devices in response toimpairments the devices experience in communicating using the network;aggregating the reformatting requests collected over a period of time;analyzing the aggregated reformatting requests collected over the periodof time to identify common characteristics indicating a pattern in thereformatting requests collected over the period of time; and derivingthe data indicating the problem in the network based on the pattern. 9.The non-transitory computer readable storage device of claim 8, whereinthe devices include at least one of end user devices and networkdevices.
 10. The non-transitory computer readable storage device ofclaim 8, wherein the problem is caused by at least one of an end userdevice and a network device.
 11. The non-transitory computer readablestorage device of claim 8, wherein the problem is a lack of capacity inthe network for handling traffic.
 12. The non-transitory computerreadable storage device of claim 8, wherein the network is a multimedianetwork.
 13. The non-transitory computer readable storage device ofclaim 8, wherein the network is a cellular network.
 14. Thenon-transitory computer readable storage device of claim 8, whereinremedial actions are taken based on the derived data.
 15. A system, forderiving data indicating a problem in a network, comprising: aprocessor; and a memory storing instructions which, when executed by theprocessor cause the processor to perform operations comprising:aggregating reformatting requests received from devices in the networkover a period of time; analyzing the aggregated reformatting requestsreceived from the devices in the network over the period of time toidentify common characteristics indicating a pattern in the reformattingrequests received over the period of time, wherein the reformattingrequests are generated by the devices in response to impairments thedevices experience in communicating using the network; and deriving thedata indicating the problem in the network based on the pattern.
 16. Thesystem of claim 15, wherein the devices include at least one of end userdevices and network devices.
 17. The system of claim 15, wherein theproblem is caused by at least one of an end user device and a networkdevice.
 18. The system of claim 15, wherein the problem is a lack ofcapacity in the network handling traffic.
 19. The system of claim 15,wherein the network includes at least one of a multimedia network and acellular network.
 20. The system of claim 15, wherein remedial actionsare taken based on the derived data.